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The outbreak of the Covid-19 pandemic led to restrictions in production worldwide. Numerous firms were affected and unable to keep up production due to lockdowns. In disruptive events like this, the resilience of the production system is of central importance, as the survivability of the entire firm depends on it. In this context, the literature argues that cutting-edge technologies, such as Artificial Intelligence (AI), raise the proactive and reactive capabilities of firms, enabling them to better resist and recover from disruptive events and thus, show a higher resilience. This paper takes up this topic and observes the Covid-19 pandemic with the aim to analyse whether a firm's AI-readiness had an impact on its production resilience during the spring 2020 lockdown in Germany. For this purpose, we combine two large-scale surveys containing data from 237 manufacturers in Germany and test hypotheses based on quantitative analyses. Our results show that firms could indeed benefit from AI-enabled production during the lockdown. However, it is also clear that manufacturers have to exceed a certain AI threshold to significantly increase their resilient capabilities and realise positive effects. Our findings not only hold implications for research, but also provide recommendations for the resilience management of manufacturers. [ABSTRACT FROM AUTHOR]

In this review, the multifaceted issue of food security is addressed, emphasizing the need for innovative and culturally appropriate solutions. Exploring insect livestock farming emerges as a potential remedy, offering a pathway to alleviate food insecurity and promote food sovereignty, particularly when integrated with social acceptability. Stakeholder engagement on both production and consumption fronts, coupled with sustained support, is vital for successful implementation. The expanding landscape of commercial insect farming in the West prompts questions about its broader scalability and equitable deployment, especially for vulnerable populations. Existing research gaps underscore the need for a coordinated effort across international, national, and legal frameworks to effectively integrate insect farming into existing agricultural systems. In this review, we have delved into the industrial-scale production processes of mealworms and black soldier flies (BSFs), known for their high protein content and organic waste conversion, covering small and industrial cultivation methods, offering insights into mealworm production life cycles, innovative rearing systems, and harvesting techniques. This review concludes with climate-specific recommendations for insect facilities, stressing the importance of sustainable practices, continuous research and development, effective market strategies and economic feasibilities in Iceland. In the context of escalating demand for sustainable protein sources, industrial-scale insect production emerges as a pivotal player in addressing global food security challenges. [ABSTRACT FROM AUTHOR]

Microbial cell factories utilize renewable raw materials for industrial chemical production, providing a promising path for sustainable development. Bacillus subtilis is widely used in industry for its food safety properties, but challenges remain in the limitations of microbial fermentation. This study proposes a novel strategy based on lifespan engineering to design robust B. subtilis chassis cells to supplement traditional metabolic modification strategies that can alleviate cell autolysis, tolerate toxic substrates, and get a higher mass transfer efficiency. The modified chassis cells could produce high levels of L-glutaminase, and tolerate hydroquinone to produce α-arbutin efficiently. In a 5 L bioreactor, the L-glutaminase enzyme activity of the final strain CRE15TG was increased to 2817.4 ± 21.7 U mL− 1, about 1.98-fold compared with that of the wild type. The α-arbutin yield of strain CRE15A was increased to 134.7 g L− 1, about 1.34-fold compared with that of the WT. To our knowledge, both of the products in this study performed the highest yields reported so far. The chassis modification strategy described in this study can Improve the utilization efficiency of chassis cells, mitigate the possible adverse effects caused by excessive metabolic modification of engineered strains, and provide a new idea for the future design of microbial cell factories. [ABSTRACT FROM AUTHOR]

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While previous panel studies have focused on the linear specifications of the industrial production-carbon emissions nexus, nonlinear panel studies on this relationship remain thin on the ground. This article examines the asymmetric nexus between industrial production and carbon emissions in 30 selected Sub-Saharan African countries spanning from 1990 to 2019. In the presence of cross-sectional dependence, the second-generation unit root tests were applied to examine the unit-root properties. The cointegration tests results confirm the presence of a long-run relationship among the variables. Finally, we employed the panel nonlinear autoregressive distributed lag approach to estimate the coefficient values. Generally, the empirical findings demonstrate that industrial production asymmetrically influences carbon emissions both in the short and long-runs. Specifically, the long-run estimates indicate that a positive shock in industrial production of 1% induces an increase in carbon emissions by 0.213%, while a negative shock induces a 0.390% decrease in carbon emissions. Based on these results, there is a need for policymakers in the selected Sub-Saharan African countries to consider the asymmetric behavior of industrial production while formulating industrialization policies. These policies should also be based on the condition of adopting green technology forms of energy. JEL Classification O14, Q5, Q54 [ABSTRACT FROM AUTHOR]

There is a consensus that stock markets are procyclical. However, answers to some important questions remain unclear. Do stock markets lead or lag business cycles? More interestingly, what is the duration with which they lead or lag them? This study uses different time-series filters and time-difference analysis to answer these questions by examining the dynamic interactions between three major stock indices and key macroeconomic indicators in the United States. The findings show that stock markets have been strongly procyclical, lagging industrial production by one to three months in recent decades. There have been noteworthy changes in the relationship between inflation and stock market cycles. The correlations changed from negative in the 1980s and 1990s to positive in the 2000s and 2010s. The results also reveal close associations between the stock indices, offering new insights into the interplay between financial markets and economic cycles. [ABSTRACT FROM AUTHOR]

In this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non‐persistent factor, both as a single global factor using all data and as factors for each category of data. The in‐sample predictive performances of the three persistent factors together are better than the non‐persistent factors and the single global factors. Out‐of‐sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub‐categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1‐ to 6‐month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12‐month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate‐based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures. [ABSTRACT FROM AUTHOR]

The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment. [ABSTRACT FROM AUTHOR]

In this paper, we investigate house price shocks on the macroeconomic variables (financial market, inflation, and real sector) of the G7 economies. We use the GVAR to capture the spillover effects from the U.S. housing market and oil prices on these economies from 1991M3–2022M10. We identify the U.S. house price shock using the Structural Generalized Impulse Response Function, house supply and demand variables, and regional divergence. We find that the domestic stock markets and industrial production are the most sensitive to house price shocks. We further compare the importance of house and oil prices on domestic fluctuations. The estimates reinforce the previous findings: U.S. house prices are responsible for a quarter of the domestic volatility of the stock markets and industrial production. In the other macroeconomic segments, the effects of house prices are present, but in lower values. Our results show that house prices provoke more domestic fluctuations than oil prices. Finally, we also found that short and long‐term credit markets, as well as stock markets, transmit the house price shock to industrial production. Consequently, we provide potential channels to comprehend the spillover effect of U.S. house prices on international markets. [ABSTRACT FROM AUTHOR]

As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying on gradient data. Among these, the tree-seed algorithm (TSA) distinguishes itself due to its unique mechanism and efficient searching capabilities. However, an imbalance between its exploitation and exploration phases can lead it to be stuck in local optima, impeding the discovery of globally optimal solutions. This study introduces an improved TSA that incorporates water-cycling and quantum rotation-gate mechanisms. These enhancements assist the algorithm in escaping local peaks and achieving a more harmonious balance between its exploitation and exploration phases. Comparative experimental evaluations, using the CEC 2017 benchmarks and a well-known metaheuristic algorithm, demonstrate the upgraded algorithm's faster convergence rate and enhanced ability to locate global optima. Additionally, its application in optimizing reservoir production models underscores its superior performance compared to competing methods, further validating its real-world optimization capabilities. [ABSTRACT FROM AUTHOR]

The global demand for improvement in the welfare conditions of broilers has generated the necessity to implement alternative rearing systems as well as less intensive growth hybrids. The majority of the data on alternative farming methods, notwithstanding their abundance, are the result of small-scale experiments. The present extended field study examined the effect of two different industrial farming systems on broilers' antioxidant status, performance, and meat quality, including 13 replicates of each industrial breeding system (intensive conventional; free range) and two different chicken genotypes (fast growth; slow growth). The duration of the study was 51 months, and the total number of broilers was 260.000 for the conventional and 78.000 for the free-range system. The results showed that fast-growth chicks demonstrated a more satisfactory performance (in terms of body weight gain (BWG) and feed conversion ratio (FCR) with p ≤ 0.001), reduced serum lipid oxidation (p ≤ 0.05), and more tender meat. Contrarily, slow-growth chickens presented significantly higher total antioxidant capacity (TAC) in serum and thigh muscle (p ≤ 0.001), significantly lower (p ≤ 0.05) thigh muscle oxidation (in terms of thiobarbituric acid reactive substances, TBARS), increased protein and decreased fat content (p ≤ 0.05), and better smell, taste, color, and texture. In conclusion, the free-range farming system for slow-growth chickens may result in an overall higher nutritional value, sensory score, and serum and thigh muscle antioxidant profile than the conventional farming system for fast-growth broilers. However, fast-growth broilers exhibit better performance and might undergo less stress. [ABSTRACT FROM AUTHOR]

The primary objective of the study is to determine how environmental performance is affected by several factors, including trade freedom, industrial production, urbanization, financial inclusion, and energy efficiency. This research provides a novel perspective on the environmental performance of Belt and Roads Initiatives (BRI) member states by employing panel data from 1991 to 2021. It contributes to our understanding of the role of several factors in improving environmental performance. Panel data analysis also incorporates the CD, CIPS, FMOLS, DOLS, and PMG-ARDL tests. This research possesses substantial theoretical ramifications, as it might enable the expansion and refinement of current economic and environmental theories. As per the findings of this research, each factor significantly affects environmental performance (ecological foot-point). This contributes to short-term environmental issues by conserving resources and lowering emissions, creating the framework for longterm environmentally friendly procedures and innovation. Strong environmental policies that may cause effective environmental performance in Belt and Roads Initiatives (BRI) countries. [ABSTRACT FROM AUTHOR]

Bu çalışmada, Türkiye’deki turizm gelirleri ve sanayi üretimi arasındaki ilişki 2012M1-2023M6 dönemi aylık verileri kullanılarak incelenmiştir. Değişkenler arasındaki ilişki modeldeki kademeli yapısal kırılmaları dikkate alan Fourier Vektör Otoregresyon (VAR) modeliyle tahmin edilmiştir. Elde edilen etki-tepki fonksiyonlarına göre, sanayi üretim endeksinin turizm gelirlerindeki bir şoka verdiği tepki ilk 9 ay pozitiftir. Turizm gelirlerinin sanayi üretim endeksinde meydana gelen bir şoka verdiği kümülatif tepki de pozitif olarak ölçülmüştür. Bununla birlikte, sanayi üretim endeksinin öngörü hata varyansında turizm gelirlerinin payı oldukça yüksektir; ilk 6 aylık periyotta, bu değişkenin hata varyansını açıklama gücü %30’ları geçmiştir. Fourier Granger nedensellik testi sonuçlarına göre ise Türkiye’de turizm gelirlerinden sanayi üretim endeksine doğru tek yönlü bir nedensellik ilişkisi tespit edilmiştir.

This study investigated stakeholders' perspectives on the viability of transitioning from domestic to industrial palm oil production in Nigeria. The analysis encompassed the views of key players in the palm oil value chain, including oil palm farmers, processors, and fabricators. A multi-stage sampling approach was used to select respondents from six states – Akwa Ibom, Edo, Imo, Ondo, Osun, and Kogi – based on their significance in the oil palm value chain. Farmers exhibited a positive inclination towards producing palm fruit for Special Palm Oil (SPO), emphasizing the economic benefits such as increased employment and contributions to local and national economies. However, challenges like cost implications, operational difficulties, and the perception that SPO is exclusive to large commercial farmers emerged as potential barriers. Processors showed optimism about SPO production, highlighting its potential for employment creation and economic growth. Yet, skepticism lingered, particularly regarding formal literacy requirements and economic viability compared to Technical Palm Oil (TPO). Concerns about cultural compatibility and marketing challenges were also raised. Fabricators expressed favorable perceptions of technologies related to SPO production, emphasizing the employment and economic opportunities linked with fabricating equipment for SPO. However, concerns about machine demand and potential marketing difficulties surfaced. The study underscores the importance of understanding and addressing stakeholders' perceptions for a successful transition. It suggests that heightened awareness, coupled with an emphasis on the economic and cultural benefits of SPO production, can facilitate a smooth shift from domestic to industrial palm oil production in Nigeria.

The relevance of the study is due to the fact that under the conditions of sanctions there are problems not only with the arrival of imported raw materials and goods to the Russian market for the uninterrupted process of industrial production, but also with the processes of supplying Russian oil and gas abroad. In this regard, the paper is aimed at identifying and analyzing exis ting and new opportunities for the full and efficient operation of Russian industry. The leading approach (or method) to studying the problem is deve loping measures for new sales and supply channels formation for the Russian oil and gas industry further development. Oil and gas products supply vo lumes abroad before and after sanctions implementation have been assessed. Based on the results of the study and scientists’ works, it has been concluded that in the future it will be necessary to continue the search for new ways of both supply and realization of oil and gas raw materials and products for the Russian industrial production stable operation in the near future.

This research investigates the repercussions of the global COVID-19 pandemic on the secondary sector of Côte d‘Ivoire during the period 2020–2022. Using two-stage generalized random effects (G2SLS) instrumental variable (IV) models is a statistical method used in econometrics, particularly in the context of instrumental variable (IV) models. In the context of the study, G2SLS helps in establishing the causal impact of the COVID-19 pandemic on the secondary sector while accounting for potential endogeneity issues. This study comprehensively analyzes the effects of COVID-19, and economic performance, country factors, structural characteristics, responses to fiscal and monetary policies, and overall growth in industrial production. Expanding its focus to a comparative analysis with Nigeria, Egypt, South Africa, China, and France, this research uses a robust econometric approach to unravel the complex dynamics of the secondary sector. From a cross-national perspective, this study provides nuanced insights for policymakers and stakeholders. The findings of this study could contribute to secondary sector public policy strategy-making and instruments definitions to resist similar threats.

Capsaicin, the main bioactive compound in chili peppers, is widely known for its diverse pharmacological effects, including antioxidant, anti-inflammatory, and anticancer effects. Despite its therapeutic potential, the low yield of natural capsaicin and the challenges in producing it on a large-scale limit broader industrial and clinical applications. This review provides a comprehensive analysis of capsaicin's biosynthesis in plants, chemical and enzymatic synthesis methods, and recent advancements in green production technologies. In addition, innovative applications such as drug delivery systems using nanoencapsulation and micelles are being developed to improve the bioavailability and therapeutic efficacy of capsaicin. Key findings highlight the use of capsaicin in food preservation, packaging, and pharmaceutical formulations. Future research should prioritize the refinement of synthetic routes, innovative delivery technologies, and the development of sustainable industrial processes to fully exploit the therapeutic and commercial potential of capsaicin.

This study aims to explore the relationship between tourism revenues and industrial production in the Turkish economy using monthly data for the period 2012M1-2023M6. The relationship between the variables is estimated through the Fourier Vector Autoregression (VAR) model, which takes into account gradual structural breaks in the model. According to the impulse-response functions, the response of the industrial production index to a shock in tourism revenues is positive for the first 9 months. Besides, the cumulative response of tourism revenues to a shock in the industrial production index is also estimated as positive. In addition, the share of tourism revenues in the forecast error variance of the industrial production index is quite high; in the first 6 months, the explanatory power of this variable exceeds 30%. According to the results of the Fourier Granger causality test, a one-way causality relationship is found from tourism revenues to industrial production index in Türkiye. [ABSTRACT FROM AUTHOR]

Most of the macro-literature on uncertainty has focused on macro-uncertainty caused by real activity as a source of economic fluctuations. Economic uncertainty reduces total demand in the economy via a conventional channel that is associated with real option theory. Given the findings of the existing literature, financial uncertainty other than macroeconomic uncertainty matters more for business cycle fluctuations. This study seeks to answer the following questions: Is uncertainty the primary cause of the business cycle's fluctuations? Alternatively, does it matter what kind of uncertainty exists? The research utilized the generalized linear model (GLM) and the Bayesian generalized linear model (BGLM) to analyze a dataset covering the time from July 1960 to April 2015 in the United States. Elevated levels of macroeconomic uncertainty, akin to real uncertainty, and economic policy uncertainty, as measured by news sources, demonstrate a counter-cyclical pattern in relation to business cycles. Low levels of uncertainty have a positive impact on business cycles, leading to an increase in industrial production. Conversely, high levels of uncertainty have a negative effect on business cycles, causing a decline in industrial output. We are of the opinion that high levels of macroeconomic uncertainty have a ripple effect on the entire economy, which may stifle investments, reduce consumption, and create unemployment, which is likely to influence labor participation. [ABSTRACT FROM AUTHOR]

This study examines the state-dependent effects of crude oil price shocks on the US aggregate economy. Using a smooth transition vector autoregressive model, we assess the effects of normalized supply and demand shocks in the global oil market over the different phases of the business cycle. Our findings show that the link between oil prices and macroeconomic dynamics is nonlinear and that structural shocks in the oil market conditional on recessions have a greater and more persistent contractionary impact on macroeconomic variables including industrial production, employment, and consumer price inflation. Forecast error variance decomposition confirms that oil prices have much stronger stagflationary effects during recessions. [ABSTRACT FROM AUTHOR]

With the growing need for accurate timekeeping, the demand for clocks on public buildings increased in the 19th century. The only way to meet the demand, however, was to produce individual items by hand. Due to the conservative and protectionist influence of the clockmaker’s guilds and the absence of a specialised clockmaking education, technical innovations from abroad were slow to be adopted. Despite this, there are significant innovative watchmaking personalities, such as Josef Božek at the beginning and Václav Krečmer in the second half of the century. There were also several attempts at factory mass production of tower clocks, which, however, were not developed until the 1880s by the company of Ludvík Hainz.

The implementation of ecopreneurship is evolving to address environmental sustainability challenges posed by industrial pollution. Scholars are exploring ways to enhance ecopreneurship implementation globally. However, there's a gap in understanding how ecopreneurship affects sustainability across economic and environmental dimensions. This study aims to explore the impact of ecopreneurship on sustainability, considering both economic and environmental indicators. The investigation was conducted through Analytical Hierarchical Process (AHP) methodology covering 102 ecopreneurs operating in the Tanzania food and beverage industry. The results of the study showed that implementation of ecopreneurship results in the attainment of sustainability through composite scores of 79.5%. In conclusion, this study underscores the significant impact of ecopreneurship implementation on composite sustainability, as evidenced by notable reductions in environmental emissions and improvements in economic performance within the studied industries. The findings highlight the importance of integrating environmental considerations into entrepreneurial endeavors for achieving sustainability goals. It is recommended that stakeholders actively promote and incentivize the adoption of ecopreneurship practices, invest in eco-friendly technologies, foster collaboration, implement effective monitoring mechanisms, and integrate sustainability into business strategies. The study recommends stakeholders' efforts directed towards enhancing the implementation of ecopreneurship practices in line with the United Nations Sustainable Development Goals (SDGs, 2015–2030), specifically goal nine (9) on pollution prevention through the implementation of Eco practices-based solutions to foster innovations and sustainable industrialization. Thus, strategizing the implementation of ecopreneurship practices is considered to have an essential contribution to addressing the challenges of sustainability and therefore enriching the realization of the global sustainable development agenda.

The primary objective of the study is to determine how environmental performance is affected by several factors, including trade freedom, industrial production, urbanization, financial inclusion, and energy efficiency. This research provides a novel perspective on the environmental performance of Belt and Roads Initiatives (BRI) member states by employing panel data from 1991 to 2021. It contributes to our understanding of the role of several factors in improving environmental performance. Panel data analysis also incorporates the CD, CIPS, FMOLS, DOLS, and PMG-ARDL tests. This research possesses substantial theoretical ramifications, as it might enable the expansion and refinement of current economic and environmental theories. As per the findings of this research, each factor significantly affects environmental performance (ecological foot-point). This contributes to short-term environmental issues by conserving resources and lowering emissions, creating the framework for long-term environmentally friendly procedures and innovation. Strong environmental policies that may cause effective environmental performance in Belt and Roads Initiatives (BRI) countries.

The ongoing digitization of online learning resources has led to a proliferation of collaboration platforms for specific areas of application and disciplines. Simultaneously, especially manufacturing companies need to gain and secure knowledge in the field of Industrial Data Science (IDS) and to collaborate with partners to form a competitive value chain. In this paper, collaborative and competency-based requirements for applying industrial data analytics are adapted into specifications for implementing a collaboration platform. The currently absent requirements of IDS projects are defined and then turned into platform-specific functions. In an ongoing research project the functions are applied in an online platform. The usage in a system of dynamic value networks validates the defined requirements in a practical environment. The innovation of the platform is its clear focus on IDS project practitioners, who are typically comprised of several different domains. It secures a long-term use of deployed data analytics solutions in the industrial environment. The first version of the developed collaboration platform is available online and still in validation.

The study assesses main factors affecting successful processes of industrial production in Russian regions in recent times. It considers the unfavorable effects of the pandemic and sanctions on industrial enterprises production. The discussion centers on the examination of industrial production in modern Russia and forecasting its further development. The introduction presents a review of scientific literature of financial experts and researchers studying the phenomena of industrial production. The purpose of the study is to examine the state of industrial production development in Russia considering regional economic evolution under sanctions. An exhaustive assessment of the factors determining industrial development, as well as main economic indicators of industrial companies’ performance, has been carried out. The article also includes a study of the works of scientists who participated in assessing this issue from both domestic and foreign points of view. Special attention has been paid to the factors that characterize the industrial production cycle in Russia and its regions. In conclusion, the author summarized the results of the conducted research and review of scientific materials. The essence of this generalization is to endorse continuous transformation of regional industrial production management in the future. Proposals for sustainable development of industrial production in the long term have also been put forward.

The article presents a study to assess main indicators of efficiency of production process development in the manufacturing industry of the Russian Federation in recent years, related to the negative impact of the COVID-19 pandemic on industrial production and on manufacturing in general. The purpose of the study is to examine the level of manufacturing industrial production development in the Russian Federation under the conditions of sanctions. To achieve this goal, international and domestic experience of manufacturing industry economic development has been studied, which in general terms characterizes the level of competencies required for manufacturing industries effective development. A comparative assessment of such approaches has been carried out. The study used the summary and grouping methods, tabular and graphical methods of information presentation, dynamics analysis methods. The information base of the study was microdata of sample surveys of the Federal State Statistics Service. The indicators have been presented in annual periodicity for 2016–2022 for Russia. As a result of studying economic development of manufacturing industry types, their pros and cons have been established. On the basis of statistical data and sample surveys for 2022, their systematization and evaluation have been carried out. The changes that occurred in main economic indicators characterizing the manufacturing industry sphere development have been studied. The study pays attention to the indicators characterizing industry development in Russia. The article concludes that in the future it will be necessary to continue the manufacturing industry transformation on an innovative basis.

The article deals with the prospects of economic use of algae resources, which have unique biochemical properties and are valuable raw materials for many areas of industrial activity. The relevance of this topic is due to the fact that, despite the high adaptive capacity and almost ubiquitous distribution of algal resources, their economic potential is not fully realized, and this area of the economy is very promising for scientific research. Analysis of available literature showed that in spite of obvious perspectives of expanding sphere of algae resources use, Russian businessmen still leave without proper attention possibilities of algae use as raw material for different industrial processes. At the same time, the Far Eastern and Northern sea areas with particularly rich reserves of algae resources, which are very promising from the point of view of their economic development, have now been identified. The examples presented in this paper demonstrate the high potential prospects for expanding the economic use of algae resources in Russia.

Introduction. Despite the transition of economic systems to a digital economy, today industry is a driver of the development of the economy of any country, and accordingly, consideration of the development peculiarities of this sphere in post-Soviet countries is of scientific interest. Hence, the purpose of the study is to analyze the industry development dynamics in the post-Soviet countries in the period of 1990–2021. Materials and methods. In the course of the study, methods such as analysis and synthesis, tabular and graphical, relative and average values were used. Results. At the start of the market transformation, post-Soviet countries had high industrial potential, while the share of the GVA industry in the GDP of each subject was above 20%. As we move away from 1990, more post-Soviet countries are showing a tendency to reduce the GVA ratio, which is due to the combination of factors, the main of which is the impact of the transformational transition and subsequent crises. The best results in hydrocarbon production are shown by countries with natural resource deposits: Russia, Kazakhstan, Azerbaijan and Turkmenistan, the rest of the post-Soviet countries have near-zero production indicators, which reduces their ability to invest “petrodollars” in other sectors of the economy. Indirectly, the import structure shows problems in the manufacturing industry, in which in 2021 the import of machinery and equipment prevails in almost all post-Soviet countries. Conclusions. Summarizing the results obtained during the study, we can draw a general conclusion based on the results of the analysis: having some accumulated (formed) capital in industry, the post-Soviet countries were unable to convert it into sustainable development of their economic systems and at the moment are largely forced to rely on imports of machinery, equipment and vehicles. The findings will be useful to researchers in the field of post-Soviet countries’ integration and disintegration

The article presents an assessment of the main statistical indicators related to the efficiency of industrial production growth in various regions of Russia over the past few years. The inclusion of digital achievements in the context of the negative consequences of the recent pandemic and sanctions on the production of industrial enterprises is also considered. The research is devoted to the application of digital technologies in industrial production in Russia in several regions under current circumstances and the potential of their future development. The introduction contains an overview of the scientific literature of economists and researchers dealing with industrial production and digitalization. This section also outlines the goals and objectives of the study. The purpose of the study is to study the current state of industrial production growth in Russian regions, depending on the development of the regional economy under sanctions. The assessment of industrial growth indicators of the main economic indicators of industrial enterprises is carried out. There was also a study of methods and researchers who contributed to the consideration of this issue both at the national and international level. The article considered indicators illustrating the growth of Russian industrial production and its regional variations, as well as evaluating the use of digital technologies in industrial production. At the conclusion of the article, based on the scientific work carried out and a review of the literature of researchers who studied this issue, the result was summed up. The essence of this conclusion suggests that the reorganization of industrial production at the level of regional management should continue in the foreseeable future. Additionally, a long-term forecast for the development of industrial production has been compiled.

The impact of oil price on the aggregate economy is an interesting topic which has been examined mainly in the time dimension, but relatively little exploration has been done to reveal how oil price influences inflation, industrial production, and unemployment across frequencies and over time. Employing a continuous wavelet approach, this study contributes to the literature by investigating the effects of oil price on aggregate economic activities in the time-frequency space with a monthly dataset from South Korea. The empirical results demonstrate that: First, the effect of oil price on inflation becomes stronger as moving from high frequencies between 2000 and 2005 to low frequencies between 2008 and 2018. Second, an increase in oil price is associated with a decrease in industrial production, and oil price movements could largely mirror industrial production fluctuations at the 64-month scale between 2011 and 2013 and at the 64-96-month scale between 2014 and 2021. Third, the impact of oil price on unemployment becomes weaker when moving from the 12-24-month scale between 2009 and 2012 to the 24-48-month scale between 2008 and 2016. Our results indicate that policymakers should consider these heterogeneous effects of oil price on the aggregate economy while developing stabilization policies. [ABSTRACT FROM AUTHOR]

The digitization of learning resources has led to an increase in specialized collaboration platforms across various fields, including the need for manufacturing companies to develop and maintain expertise in Industrial Data Science (IDS). This paper presents an approach to integrating collaborative and competency-based needs specific to industrial data analytics into a functional collaboration platform. We define the unique requirements of IDS projects and translate them into platform features. These features are then implemented and tested in an online platform within a research project, validating their effectiveness in a dynamic value network setting. The platform's primary innovation lies in its tailored design for IDS project practitioners from diverse domains, ensuring sustainable integration of data analytics in industrial settings. The initial version of this collaborative platform is currently accessible online and undergoing validation. [ABSTRACT FROM AUTHOR]

We develop a method for constructing prediction intervals for a nonstationary variable, such as GDP. The method uses a Factor Augmented Regression (FAR) model. The predictors in the model include a small number of factors generated to extract most of the information in a set of panel data on a large number of macroeconomic variables that are considered to be potential predictors. The novelty of this article is that it provides a method and justification for a mixture of stationary and nonstationary factors as predictors in the FAR model; we refer to this as mixture-FAR method. This method is important because typically such a large set of panel data, for example the FRED-QD, is likely to contain a mixture of stationary and nonstationary variables. In our simulation study, we observed that the proposed mixture-FAR method performed better than its competitor that requires all the predictors to be nonstationary; the MSE of prediction was at least 33% lower for mixture-FAR. Using the data in FRED-QD for the United States, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production. We observed that the mixture-FAR method performed better than its competitors. [ABSTRACT FROM AUTHOR]

Dried and boiled sheep meat products made according to traditional technologies are highly valued by the people of the Middle East, Central Asia and the Balkan countries. In recent years, the industry's interest in the production of these products has been growing by applying traditional production technologies in industrial conditions while meeting high levels of hygiene requirements and producing products with standardized characteristics. The acceptability of these products by consumers accustomed to certain properties of traditional products mostly depends on the sensory characteristics of industrially produced products. Therefore, in this work, the goal is to examine the sensory properties of dried and boiled sheep meat products, during the production of which eight different treatments were used. As said before, materials for this research were dried and boiled sheep meat products produced by eight different treatments. For research in this work, meat obtained from the Pramenka breed sheep, which were raised in a rural household, was used. Goats moved freely and fed on mountain pastures. The slaughtering and bleeding of the sheep was done in the local meat industry. After bleeding, the carcasses were cooled for 24 hours at a temperature of 1°C, and after being separated from the carcasses, the thigh meat was cut into pieces of equal size. After brining, the pieces of meat are heat-treated at boiling temperature or smoked and dried. Meat samples taken from pieces of finished products were used for chemical and sensory analysis. Sensory evaluation was conducted in the sensory evaluation laboratory by six trained evaluators (professors and faculty assistants). During the evaluation, a test of descriptive analysis was used. The obtained results were processed statistically. ANOVA was used to examine significant differences between treatments, and statistically significant differences between treatments were examined with the Tukey test (a = 0.05, p < 0.05 and a = 0.01, p < 0.01). The impact of different heat treatment procedures on the health safety of products was determined based on the content of heavy metals (ICP OES was used for the analysis), and PAHs (GC-MS was used for the analysis) in the finished products. Based on the overall score for sensor quality, the boiled and dried products met the evaluator's requirements. Average scores for boiled products were slightly higher than for dried products, although the difference was small. The overall sensory score influenced the selection of some experimental groups as treatments in which the products received the highest scores. Four out of 6 evaluators gave the highest rating (92.33 - 96.33%) to the samples of boiled sheep meat products obtained under the following conditions: time 1 hour and 47 minutes and temperature 65 - 75 °C. The majority of evaluators (4 out of 6) gave the highest score to sheep meat samples (89 - 95.33%), which were obtained by the drying process under the following conditions: drying time 2 hours at a temperature of 14 - 15 °C, smoking time 5 hours at temperature 14 - 15 °C, air circulation 40 - 50 m³/min. and air humidity 85 - 88%. The concentrations of detected heavy metals were within the permitted limit values. The obtained values were not statistically significant, which means that the temperature and time values in the processing had no significant influence on their content. The following PAH compounds were detected in sheep meat products: acenaphthylene (Acy), fluorene (Fln), phenanthrene (Phe), anthracene (Ant) and pyrene (Pyr), while the content of compound considered as toxicity indicator (benzo(a)pyrene) was not detected in the products in any of the experimental groups of boiled and dried products. The results obtained during the conducted research showed that sheep meat products made in industrial conditions according to traditional technologies have a high sensory quality, which indicates their acceptability by consumers. In addition, these products are completely safe for consumption. [ABSTRACT FROM AUTHOR]

The textile and apparel industry is one of the largest industries in the world, with significant environmental and social impacts, such as manual labor in poor working conditions and resource depletion and environmental degradation due to ineffective production. As a result, there is an increasing need for sustainable practices and a transition toward a more circular and responsible industry. In this paper, we introduce the proposed approach in the context of a sustainable transition for the industrial textiles. Instead of human labor, the automated solution to detect defects is proposed for the 4.0 revolution in general and the textile industry in particular. Specifically, the mechanical design of a three-dimensional model for small- and medium-sized fabric rolls is portrayed. Some computational mechanics are created to ensure a stable mechanism and proper actuators. For acquiring accurate data and measuring the machine state, several installations, that is, a digital camera, load cell, and positioning sensor, and the layout of the light source are improved to intensify the image quality. The control theme, such as the cross-coupling controller, offers superior performance in synchronization and vibration-less motion. Lastly, the excellent detection method is provided by the convolutional neural network scheme for offline training to distinguish defects on products. Instead of a manual check or low rate of defect detection on products, the contributions of this research are as follows: (a) analyzing the mechanical design of the textile machining system in terms of dynamic characteristics; (b) implementing a smooth scheme based on motion control synchronization; and (c) integrating various strategies, including vision-based computation and control topology, to demonstrate superior performance. These results highlight that our approach can be widely adapted to address sustainability challenges not only in the textile field but also in global manufacturing practices. [ABSTRACT FROM AUTHOR]

The article analyzes the differentiation of Russian regions in terms of the dynamics of their socioeconomic development in 2022 based on a number of statistical indicators, primarily the indices of the output of goods and services by basic types of economic activity and industrial production (taking into account the prevailing pattern of interregional differences in the dynamics and structure of gross regional product in 2020–2021). The author has demonstrated that the economic development of regions in 2022 was significantly influenced by the dependence of their economy on foreign capital, which had not previously played such a significant role. For the first time in recent years, this has led to the greatest problem in the Northwestern Federal District. In manufacturing, as in 2015, the Southern Federal District, primarily its largest regions, as well as the Central Federal District, were characterized by the highest growth rates. The latter was simultaneously distinguished by large contrasts between individual federal subjects, which proved to be both among the leaders and outsiders in terms of production indices. Real cash incomes in 2022 depended weakly on the dynamics of economic indicators'—the redistribution of incomes within the framework of state social policy traditionally played a significant role. In addition, as is typical of crisis years, the dynamics of retail trade turnover by region was not determined by real cash incomes. [ABSTRACT FROM AUTHOR]

A consistent approach to the optimal selection of replacement technologies is required in industrial production. A conceptual framework for formalized representation of production technologies is outlined. The diversity of external and internal disturbances is discussed. Types of technological substitution are classified. The structure of a decision support system (DSS) and the content of its intellectual core are defined in the form of a high-level mathematical toolkit. Practical experience with the proposed tools in technological replacement is outlined. [ABSTRACT FROM AUTHOR]

This paper tests for convergence of EMU inflation rates and industrial production by testing for the existence of fractional cointegration relations. The notion of fractional cointegration allows for long-term equilibria with a higher degree of persistence than allowed for in the standard cointegration framework. Over the full sample from 1999:Q1–2021:Q4, we find evidence of fractional cointegration in both inflation and industrial production among many country pairs. Our results suggest some evidence for "convergence clusters" among either core or periphery countries in the case of inflation. Similarly, we find stronger evidence of cointegration pairs among core countries for industrial production compared to periphery or mixed core–periphery groups. Testing for a break in the persistence structure, the results show evidence of a break in the persistence of both inflation and industrial production in a number of countries. Inflation persistence is substantially higher after the break, suggesting a higher potential for diverging processes during economic crises. On the contrary, for the case of industrial production, persistence is lower during the post-crisis period. [ABSTRACT FROM AUTHOR]

Zymomonas mobilis is an emerging chassis for being engineered to produce bulk products due to its unique glycolysis through the Entner–Doudoroff pathway with less ATP produced for lower biomass accumulation and higher product yield. When self‐flocculated, the bacterial cells are more productive, since they can self‐immobilize within bioreactors for high density, and are more tolerant to stresses for higher product titers, but this morphology needs to be controlled properly to avoid internal mass transfer limitation associated with their strong self‐flocculation. Herewith we explored the regulation of cyclic diguanosine monophosphate (c‐di‐GMP) on self‐flocculation of the bacterial cells through activating cellulose biosynthesis. While ZMO1365 and ZMO0919 with GGDEF domains for diguanylate cyclase activity catalyze c‐di‐GMP biosynthesis, ZMO1487 with an EAL domain for phosphodiesterase activity catalyzes c‐di‐GMP degradation, but ZMO1055 and ZMO0401 contain the dual domains with phosphodiesterase activity predominated. Since c‐di‐GMP is synthesized from GTP, the intracellular accumulation of this signal molecule through deactivating phosphodiesterase activity is preferred for activating cellulose biosynthesis to flocculate the bacterial cells, because such a strategy exerts less perturbance on intracellular processes regulated by GTP. These discoveries are significant for not only engineering unicellular Z. mobilis strains with the self‐flocculating morphology to boost production but also understanding mechanism underlying c‐di‐GMP biosynthesis and degradation in the bacterium. [ABSTRACT FROM AUTHOR]

The purpose of this study is to measure the impact of the Syrian war on the development of the private industrial sector in the Syrian governorates. The study used the difference-in-differences methodology to compare changes in industrial production between the Syrian governorates that were exposed to the war and those that were not. The study developed the application of the difference-in-differences methodology from the least squares estimator to the dynamic least squares estimator. The DOLS estimator is characterized by its ability to deal with unstable, cross-sectionally correlated, heterogeneously varying data, and autocorrelation in time series variables. This estimator was applied to several analyses in the methodology related to trend effects, group effects, individual effects, and duration effects. The study found that the Syrian war had a significant negative impact on the development of the private industrial sector in Syrian governorates that were exposed to the war. Specifically, the study found that industrial production in Syrian governorates that were exposed to the war decreased by more than 50% from its pre-war level of development. These results are due to several reasons, including infrastructure destruction, as the war caused damage to industrial infrastructure, including factories and production lines, as well as disruption of supply chains due to economic sanctions. This study allows the application of the DOLS estimator within the difference-in-differences methodology to data from different sectors in Syria, provided that the data is available, allowing us to determine the standing-point (where are we now) by measuring and understanding the impacts of the war accurately on the development of vital sectors in the economy. The study also recommends the need to conduct a new survey of the private industrial sector in Syria, thus measuring the duration of the extension of the war’s impacts and filling the gap of previous years through the technique proposed in this study. The tool is important for inferring the multifaceted impacts of the war on Syria for policymakers and investors interested in the development of the private industrial sector in Syria.

The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.

As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying on gradient data. Among these, the tree-seed algorithm (TSA) distinguishes itself due to its unique mechanism and efficient searching capabilities. However, an imbalance between its exploitation and exploration phases can lead it to be stuck in local optima, impeding the discovery of globally optimal solutions. This study introduces an improved TSA that incorporates water-cycling and quantum rotation-gate mechanisms. These enhancements assist the algorithm in escaping local peaks and achieving a more harmonious balance between its exploitation and exploration phases. Comparative experimental evaluations, using the CEC 2017 benchmarks and a well-known metaheuristic algorithm, demonstrate the upgraded algorithm’s faster convergence rate and enhanced ability to locate global optima. Additionally, its application in optimizing reservoir production models underscores its superior performance compared to competing methods, further validating its real-world optimization capabilities.